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arXiv:2608. 03731v1 Announce Type: new Abstract: Patient-facing medical LLMs and agents increasingly answer symptom questions before clinician contact, where the key safety question is what action the user should take next.
arXiv:2609.14823v1 Announce Type: new Abstract: Multimodal clinical decision-making requires reliable reasoning over heterogeneous evidence from electronic health records, medical images, and physiol...
arXiv:2609.00296v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly proposed for healthcare tasks such as clinical documentation, evidence retrieval, patient messaging,...
The paper introduces a benchmark of 400 synthetic healthcare workflow episodes to evaluate large language model (LLM) agents’ reliability in escalating care when evidence is missing or conflicting. It finds that while clean decision accuracy is high (0.97), models miss a large proportion of necessary escalations (32–97%) under uncertainty, and even the largest 9.7B model’s performance drops to 0.78 in conflict scenarios. A workflow-aware controller improves utility and reduces missed escalations, but its effectiveness is limited by lexical generalization failures.
TRACER is a training‑free multimodal harness that builds minute‑level action records from continuous video and merges them with clinical data into hourly reports, enabling an LLM agent to query and inspect both modalities. In evaluation, TRACER achieved up to 84.8% accuracy on action detection and 61.0% accuracy on clinician‑informed QA, outperforming the strongest baseline. Across 280 patients and ~60,000 camera‑hours, TRACER detected significantly less purposeful activity in patients with a positive delirium assessment compared to those without (p < 0.0001).
arXiv:2607. 09142v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in online medical consultation, yet existing benchmarks remain poorly aligned with real clinical practice.